The influence of comorbid personality disorders on recovery from depression
Received 9 January 2015
Accepted for publication 27 January 2015
Published 16 March 2015 Volume 2015:11 Pages 725—732
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Professor Wai Kwong Tang
Tinakon Wongpakaran, Nahathai Wongpakaran, Vudhichai Boonyanaruthee, Manee Pinyopornpanish, Suthi Intaprasert
Department of Psychiatry, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
Purpose: The impact of personality disorders on the treatment of and recovery from depression is still a controversial topic. The aim of this paper is to provide more information on what has led to this disagreement.
Materials and methods: Clinician-rated Hamilton Depression Rating Scale (HAMD) scores were assessed among 82 depressed outpatients who were receiving a routine treatment combination of antidepressant medication and psychosocial intervention. The participants were followed up over five visits at 3-month intervals: at the baseline, at 3, 6, 9 and 12 months. Personality disorders were assessed after the last visit in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. These repeated measures were used to explore the impact of personality disorders on HAMD scores by using a linear mixed model.
Results: Among the four personality clusters that were used (A, B, C, and mixed), only those in cluster B and in the mixed cluster were found to take significantly longer than those without personality disorders, for reduction in HAMD scores over the course of treatment.
Conclusion: In this study, the impact of personality disorders on treatment outcomes varied with the way that the personality disorder variables were described and used as independent predictors. This is because the outcomes were influenced by the impact weight of each personality disorder, even within the same cluster.
Keywords: depressive disorder, mixed linear model, impact, multilevel analysis
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